The course "Implementing Artificial Neural Networks with TensorFlow" covers the theoretical basis of a wide range of different network types and their implementation in TensorFlow.

Aim of the course

The aim of the course is to create an elaborate understanding of the theory behind artificial neural networks including all necessary mathematical knowledge. Further, current state of the art network types and approaches are introduced. A main focus is on the implementation of simple versions of the most frequently used networks and algorithms with the help of python and TensorFlow.

Goal

At the end of the course, participants shall have sufficient knowledge to design artificial neural networks on a theoretical basis to solve real-world problems. Further, they shall be able to implement and debug their networks.

Prerequisites

Don't get discouraged by these prerequisites. Most of the things you can learn on the go!

Python

You don't know how to write code in python yet? Then it is high time to start learning it: It is a beautiful, fun and easy to learn programming language. Even though it isn't as accessible as it used to be and it teaches python 2.7 (the course uses python 3.x) codecademy's interactive python tutorial is still a good place to start. Once you finished the tutorial, you should be able to implement or at least understand most parts of the assignment's code.